Iron oxide nanoparticles(IONPs)have wide applications in the biomedical field due to their outstanding physical and chemical properties.However,the potential adverse effects and relatedmechanisms of IONPs in human org...Iron oxide nanoparticles(IONPs)have wide applications in the biomedical field due to their outstanding physical and chemical properties.However,the potential adverse effects and relatedmechanisms of IONPs in human organs,especially the lung,are still largely ignored.In this study,we found that group-modified IONPs(carboxylated,aminated and silica coated)induce slight lung cell damage(in terms of the cell cycle,reactive oxygen species(ROS)production,cell membrane integrity and DNA damage)at a sublethal dosage.However,aminated IONPs could release more iron ions in the lysosome than the other two types of IONPs,but the abnormally elevated iron ion concentration did not induce ferroptosis.In-triguingly,amino-modified IONPs aggravated the accumulation of intracellular peroxides induced by the ferroptosis activator RSL3 and thus caused ferroptosis in vitro,and the coadministration of amino-modified IONPs and RSL3 induced more severe lung injury in vivo.Therefore,our data revealed that the surface functionalization of IONPs plays an important role in determining their potential pulmonary toxicity,as surface modification influences their degradation behavior.These results provide guidance for the design of future IONPs and the corresponding safety evaluations and predictions.展开更多
Flexible pressure sensors are unprecedentedly studied on monitoring human physical activities and robotics.Simultaneously,improving the response sensitivity and sensing range of flexible pressure sensors is a great ch...Flexible pressure sensors are unprecedentedly studied on monitoring human physical activities and robotics.Simultaneously,improving the response sensitivity and sensing range of flexible pressure sensors is a great challenge,which hinders the devices’practical application.Targeting this obstacle,we developed a Ti_(3)C_(2)T_(x)-derived iontronic pressure sensor(TIPS)by taking the advantages of the high intercalation pseudocapacitance under high pressure and rationally designed structural configuration.TIPS achieved an ultrahigh sen-sitivity(S_(min)>200 kPa^(−1),S_(max)>45,000 kPa^(−1))in a broad sensing range of over 1.4 MPa and low limit of detection of 20 Pa as well as stable long-term working durability for 10,000 cycles.The practical application of TIPS in physical activity monitoring and flexible robot manifested its versatile potential.This study provides a demonstration for exploring pseudocapacitive materials for building flexible iontronic sensors with ultrahigh sensitivity and sensing range to advance the development of high-performance wearable electronics.展开更多
To realize a hyperconnected smart society with high productivity,advances in flexible sensing technology are highly needed.Nowadays,flexible sensing technology has witnessed improvements in both the hardware performan...To realize a hyperconnected smart society with high productivity,advances in flexible sensing technology are highly needed.Nowadays,flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device’s software.Significant research efforts have been devoted to improving materials,sensing mechanism,and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology.Meanwhile,advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors.Machine learning(ML)as an important branch of artificial intelligence can efficiently handle such complex data,which can be multi-dimensional and multi-faceted,thus providing a powerful tool for easy interpretation of sensing data.In this review,the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented.Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated,which includes health monitoring,human-machine interfaces,object/surface recognition,pressure prediction,and human posture/motion identification.Finally,the advantages,challenges,and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed.These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.展开更多
Technological advancements in recent decades have greatly transformed the field of material chemistry.Juxtaposing the accentuating energy demand with the pollution associated,urgent measures are required to ensure ene...Technological advancements in recent decades have greatly transformed the field of material chemistry.Juxtaposing the accentuating energy demand with the pollution associated,urgent measures are required to ensure energy maximization,while reducing the extended experimental time cycle involved in energy production.In lieu of this,the prominence of catalysts in chemical reactions,particularly energy related reactions cannot be undermined,and thus it is critical to discover and design catalyst,towards the optimization of chemical processes and generation of sustainable energy.Most recently,artificial intelligence(AI)has been incorporated into several fields,particularly in advancing catalytic processes.The integration of intensive data set,machine learning models and robotics,provides a very powerful tool in modifying material synthesis and optimization by generating multifarious dataset amenable with machine learning techniques.The employment of robots automates the process of dataset and machine learning models integration in screening intermetallic surfaces of catalyst,with extreme accuracy and swiftness comparable to a number of human researchers.Although,the utilization of robots in catalyst discovery is still in its infancy,in this review we summarize current sway of artificial intelligence in catalyst discovery,briefly describe the application of databases,machine learning models and robots in this field,with emphasis on the consolidation of these monomeric units into a tripartite flow process.We point out current trends of machine learning and hybrid models of first principle calculations(DFT)for generating dataset,which is integrable into autonomous flow process of catalyst discovery.Also,we discuss catalyst discovery for renewable energy related reactions using this tripartite flow process with predetermined descriptors.展开更多
Surface functionalization of Cu-based catalysts has demonstrated promising potential for enhancing the electrochemical CO_(2)reduction reaction(CO_(2)RR)toward multi-carbon(C2+)products,primarily by suppressing the pa...Surface functionalization of Cu-based catalysts has demonstrated promising potential for enhancing the electrochemical CO_(2)reduction reaction(CO_(2)RR)toward multi-carbon(C2+)products,primarily by suppressing the parasitic hydrogen evolution reaction and facilitating a localized CO_(2)/CO concentration at the electrode.Building upon this approach,we developed surface-functionalized catalysts with exceptional activity and selectivity for electrocatalytic CO_(2)RR to C_(2+)in a neutral electrolyte.Employing CuO nanoparticles coated with hexaethynylbenzene organic molecules(HEB-CuO NPs),a remarkable C_(2+)Faradaic efficiency of nearly 90%was achieved at an unprecedented current density of 300 mA cm^(-2),and a high FE(>80%)was maintained at a wide range of current densities(100-600 mA cm^(-2))in neutral environments using a flow cell.Furthermore,in a membrane electrode assembly(MEA)electrolyzer,86.14%FEC2+was achieved at a partial current density of 387.6 mA cm^(-2)while maintaining continuous operation for over 50 h at a current density of 200 mA cm^(-2).In-situ spectroscopy studies and molecular dynamics simulations reveal that reducing the coverage of coordinated K⋅H2O water increased the probability of intermediate reactants(CO)interacting with the surface,thereby promoting efficient C-C coupling and enhancing the yield of C_(2+)products.This advancement offers significant potential for optimizing local micro-environments for sustainable and highly efficient C_(2+)production.展开更多
In the past decades,machine learning(ML)has impacted the field of electrocatalysis.Modern researchers have begun to take advantage of ML‐based data‐driven techniques to overcome the computational and experimental li...In the past decades,machine learning(ML)has impacted the field of electrocatalysis.Modern researchers have begun to take advantage of ML‐based data‐driven techniques to overcome the computational and experimental limitations to accelerate rational catalyst design.Hence,significant efforts have been made to perform ML to accelerate calculation and aid electrocatalyst design for CO_(2) reduction.This review discusses recent applications of ML to discover,design,and optimize novel electrocatalysts.First,insights into ML aided in accelerating calculation are presented.Then,ML aided in the rational design of the electrocatalyst is introduced,including establishing a data set/data source selection and validation of descriptor selection of ML algorithms validation and predictions of the model.Finally,the opportunities and future challenges are summarized for the future design of electrocatalyst for CO_(2) reduction with the assistance of ML.展开更多
Monitoring biophysical signals such as body or organ movements and other physical phenomena is necessary for patient rehabilitation.However,stretchable flexible pressure sensors with high sensitivity and a broad range...Monitoring biophysical signals such as body or organ movements and other physical phenomena is necessary for patient rehabilitation.However,stretchable flexible pressure sensors with high sensitivity and a broad range that can meet these requirements are still lacking.Herein,we successfully monitored various vital biophysical features and implemented in-sensor dynamic deep learning for knee rehabilitation using an ultrabroad linear range and highsensitivity stretchable iontronic pressure sensor(SIPS).We optimized the topological structure and material composition of the electrode to build a fully stretching on-skin sensor.The high sensitivity(12.43 kPa^(−1)),ultrabroad linear sensing range(1 MPa),high pressure resolution(6.4 Pa),long-term durability(no decay after 12000 cycles),and excellent stretchability(up to 20%)allow the sensor to maintain operating stability,even in emergency cases with a high sudden impact force(near 1 MPa)applied to the sensor.As a practical demonstration,the SIPS can positively track biophysical signals such as pulse waves,muscle movements,and plantar pressure.Importantly,with the help of a neuro-inspired fully convolutional network algorithm,the SIPS can accurately predict knee joint postures for better rehabilitation after orthopedic surgery.Our SIPS has potential as a promising candidate for wearable electronics and artificial intelligent medical engineering owing to its unique high signal-to-noise ratio and ultrabroad linear range.展开更多
Achieving carbon neutrality is an essential part of responding to climate change caused by the deforestation and over-exploitation of natural resources that have accompanied the development of human society.The carbon...Achieving carbon neutrality is an essential part of responding to climate change caused by the deforestation and over-exploitation of natural resources that have accompanied the development of human society.The carbon dioxide reduction reaction(CO_(2)RR)is a promising strategy to capture and convert carbon dioxide(CO_(2))into value-added chemical products.However,the traditional trial-and-error method makes it expensive and time-consuming to understand the deeper mechanism behind the reaction,discover novel catalysts with superior performance and lower cost,and determine optimal support structures and electrolytes for the CO_(2)RR.Emerging machine learning(ML)techniques provide an opportunity to integrate material science and artificial intelligence,which would enable chemists to extract the implicit knowledge behind data,be guided by the insights thereby gained,and be freed from performing repetitive experiments.In this perspective article,we focus on recent ad-vancements in ML-participated CO_(2)RR applications.After a brief introduction to ML techniques and the CO_(2)RR,we first focus on ML-accelerated property prediction for potential CO_(2)RR catalysts.Then we explore ML-aided prediction of catalytic activity and selectivity.This is followed by a discussion about ML-guided catalyst and electrode design.Next,the potential application of ML-assisted experimental synthesis for the CO_(2)RR is discussed.展开更多
Despite acknowledgment of structural reconstruction of materials following oxygen evolution reaction (OER) reaction, the role of support during the reconstruction process has been ignored. Given this, we directly in s...Despite acknowledgment of structural reconstruction of materials following oxygen evolution reaction (OER) reaction, the role of support during the reconstruction process has been ignored. Given this, we directly in situ transform the residual iron present in raw single-walled carbon nanotubes (SWCNT) into Fe_(2)O_(3) and thus build Fe_(2)O_(3)-CNT as the model system. Intriguingly, an anomalous self-optimization occurred on SWCNT and the derived components show satisfactory electrochemical performance. Soft X-ray absorption spectroscopy (sXAS) analysis and theory calculation correspondingly indicate that self-optimization yields stronger interaction between SWCNT and Fe_(2)O_(3) nanoparticles, where the electrons migrate from Fe_(2)O_(3) to optimized SWCNT. Such polarization will generate a positive charge center and thus boost the OER activity. This finding directly observes the self-optimization of support effect, providing a new perspective for OER and related electrochemical reactions.展开更多
Improving the catalytic activity of non-noble metal single atom catalysts(SACs)has attracted considerable attention in materials science.Although optimizing the local electronic structure of single atom can greatly im...Improving the catalytic activity of non-noble metal single atom catalysts(SACs)has attracted considerable attention in materials science.Although optimizing the local electronic structure of single atom can greatly improve their catalytic activity,it often involves in-plane modulation and requires high temperatures.Herein,we report a novel strategy to manipulate the local electronic structure of SACs via the modulation of axial Co-S bond anchored onto graphitic carbon nitride(C_(3)N_(4))at room temperature(RT).Each Co atom is bonded to four N atoms and one S atom(Co-(N,S)/C_(3)N_(4)).Owing to the greater electronegativity of S in the Co-S bond,the local electronic structure of the Co atoms is available to be controlled at a relatively moderate level.Consequently,when employed for the photocatalytic hydrogen evolution reaction,the adsorption energy of intermediate hydrogen(H*)on the Co atoms is remarkably low.In the presence of the Co-(N,S)/C_(3)N_(4)SACs,the hydrogen evolution rates reach up to 10 mmol/(g·h),which is nearly 10 and 2.5 times greater than the rates in the presence of previously reported transition metal/C_(3)N_(4)and noble platinum nanoparticles(PtNPs)/C_(3)N_(4)catalysts,respectively.Attributed to the tailorable axial Co-S bond in the SAC,the local electronic structure of the Co atoms can be further optimized for other photocatalytic reactions.This axial coordination engineering strategy is universal in catalyst designing and can be used for a variety of photocatalytic applications.展开更多
The phase transformation of catalysts has been extensively observed in heterogeneous catalytic reactions that hinder the long cycling catalysis,and it remains a big challenge to precisely control the active phase duri...The phase transformation of catalysts has been extensively observed in heterogeneous catalytic reactions that hinder the long cycling catalysis,and it remains a big challenge to precisely control the active phase during the complex conditions in electrochemical catalysis.Here,we theoretically predict that carbon-based support could achieve the phase engineering regulation of catalysts by suppressing specific phase transformation.Taken single-walled carbon nanotube(SWCNT)as typical support,combined with calculated E-pH(Pourbaix)diagram and advanced synchrotron-based characterizations technologies prove there are two different active phases source from cobalt selenide which demonstrate that the feasibility of using support effect regulating the potential advantageous catalysts.Moreover,it is worth noting that the phase engineering derived Co_(3)O_(4)-SWCNT exhibits a low overpotential of 201 mV for delivering the current density of 10 mA/cm^(2)in electrocatalytic oxygen evolution reaction(OER).Also,it reaches a record current density of 529 mA/cm^(2)at 1.63 V(vs.RHE)in the electrocatalytic urea oxidation reaction(UOR),overwhelming most previously reported catalysts.展开更多
Carbon neutrality has been proposed as a solution for the current severe energy and climate crisis caused by the overuse of fossil fuels, and machine learning(ML) has exhibited excellent performance in accelerating re...Carbon neutrality has been proposed as a solution for the current severe energy and climate crisis caused by the overuse of fossil fuels, and machine learning(ML) has exhibited excellent performance in accelerating related research owing to its powerful capacity for big data processing. This review presents a detailed overview of ML accelerated carbon neutrality research with a focus on energy management, screening of novel energy materials, and ML interatomic potentials(MLIPs), with illustrations of two selected MLIP algorithms: moment tensor potential(MTP) and neural equivariant interatomic potential(NequIP). We conclude by outlining the important role of ML in accelerating the achievement of carbon neutrality from global-scale energy management, unprecedented screening of advanced energy materials in massive chemical space, to the revolution of atomicscale simulations of MLIPs, which has the bright prospect of applications.展开更多
Protein aggregate species play a pivotal role in the pathology of various degenerative diseases.Their dynamic changes are closely correlated with disease progression,making them promising candidates as diagnostic biom...Protein aggregate species play a pivotal role in the pathology of various degenerative diseases.Their dynamic changes are closely correlated with disease progression,making them promising candidates as diagnostic biomarkers.Given the prevalence of degenerative diseases,growing attention is drawn to develop pragmatic and accessible protein aggregate species detection technology.However,the performance of current detection methods is far from satisfying the requirements of extensive clinical use.In this review,we focus on the design strategies,merits,and potential shortcomings of each class of detection methods.The review is organized into three major parts:native protein sensing,seed amplification,and intricate program,which embody three different but interconnected methodologies.To the best of our knowledge,no systematic review has encompassed the entire workflow,from the molecular level to the apparatus organization.This review emphasizes the feasibility of the methods instead of theoretical detection limitations.We conclude that high selectivity does play a pivotal role,while signal compilation,multilateral profiling,and other patient-oriented strategies(i.e.less invasiveness and assay speed)are also important.展开更多
基金supported by the National Natural Science Foundation of China(Nos.22076212 and 22222611)the Youth Innovation Promotion Association of CAS(No.2021040).
文摘Iron oxide nanoparticles(IONPs)have wide applications in the biomedical field due to their outstanding physical and chemical properties.However,the potential adverse effects and relatedmechanisms of IONPs in human organs,especially the lung,are still largely ignored.In this study,we found that group-modified IONPs(carboxylated,aminated and silica coated)induce slight lung cell damage(in terms of the cell cycle,reactive oxygen species(ROS)production,cell membrane integrity and DNA damage)at a sublethal dosage.However,aminated IONPs could release more iron ions in the lysosome than the other two types of IONPs,but the abnormally elevated iron ion concentration did not induce ferroptosis.In-triguingly,amino-modified IONPs aggravated the accumulation of intracellular peroxides induced by the ferroptosis activator RSL3 and thus caused ferroptosis in vitro,and the coadministration of amino-modified IONPs and RSL3 induced more severe lung injury in vivo.Therefore,our data revealed that the surface functionalization of IONPs plays an important role in determining their potential pulmonary toxicity,as surface modification influences their degradation behavior.These results provide guidance for the design of future IONPs and the corresponding safety evaluations and predictions.
基金These authors would like to acknowledge the financial support of the project from the National Natural Science Foundation of China(No.61904141)the funding of Natural Science Foundation of Shaanxi Province(No.2020JQ-295)+4 种基金China Postdoctoral Science Foundation(2020M673340)the Fundamental Research Funds for the Central Universities(JB210407)the Key Research and Development Program of Shaanxi(Program No.2020GY-252No.2021GY-277)National Key Laboratory of Science and Technology on Vacuum Technology and Physics(HTKJ2019KL510007).
文摘Flexible pressure sensors are unprecedentedly studied on monitoring human physical activities and robotics.Simultaneously,improving the response sensitivity and sensing range of flexible pressure sensors is a great challenge,which hinders the devices’practical application.Targeting this obstacle,we developed a Ti_(3)C_(2)T_(x)-derived iontronic pressure sensor(TIPS)by taking the advantages of the high intercalation pseudocapacitance under high pressure and rationally designed structural configuration.TIPS achieved an ultrahigh sen-sitivity(S_(min)>200 kPa^(−1),S_(max)>45,000 kPa^(−1))in a broad sensing range of over 1.4 MPa and low limit of detection of 20 Pa as well as stable long-term working durability for 10,000 cycles.The practical application of TIPS in physical activity monitoring and flexible robot manifested its versatile potential.This study provides a demonstration for exploring pseudocapacitive materials for building flexible iontronic sensors with ultrahigh sensitivity and sensing range to advance the development of high-performance wearable electronics.
基金support from National Natural Science Foundation of China(Nos.62274140,61904141,52173234)the State Key Laboratory of Mechanics and Control of Mechanical Structures(Nanjing University of Aeronautics and Astronautics)(Grant No.MCMS-E-0422G03)the Shenzhen-Hong Kong-Macao Technology Research Program(Type C,202011033000145,SGDX2020110309300301).
文摘To realize a hyperconnected smart society with high productivity,advances in flexible sensing technology are highly needed.Nowadays,flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device’s software.Significant research efforts have been devoted to improving materials,sensing mechanism,and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology.Meanwhile,advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors.Machine learning(ML)as an important branch of artificial intelligence can efficiently handle such complex data,which can be multi-dimensional and multi-faceted,thus providing a powerful tool for easy interpretation of sensing data.In this review,the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented.Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated,which includes health monitoring,human-machine interfaces,object/surface recognition,pressure prediction,and human posture/motion identification.Finally,the advantages,challenges,and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed.These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.
基金Shenzhen-Hong Kong-Macao Technology Research Programme(Type C,202011033000145)Shenzhen Excellent Science and Technology Innovation Talent Training Project-Outstanding Youth Project(RCJC20200714114435061)Functional Materials Interfaces Genome(FIG)project.
文摘Technological advancements in recent decades have greatly transformed the field of material chemistry.Juxtaposing the accentuating energy demand with the pollution associated,urgent measures are required to ensure energy maximization,while reducing the extended experimental time cycle involved in energy production.In lieu of this,the prominence of catalysts in chemical reactions,particularly energy related reactions cannot be undermined,and thus it is critical to discover and design catalyst,towards the optimization of chemical processes and generation of sustainable energy.Most recently,artificial intelligence(AI)has been incorporated into several fields,particularly in advancing catalytic processes.The integration of intensive data set,machine learning models and robotics,provides a very powerful tool in modifying material synthesis and optimization by generating multifarious dataset amenable with machine learning techniques.The employment of robots automates the process of dataset and machine learning models integration in screening intermetallic surfaces of catalyst,with extreme accuracy and swiftness comparable to a number of human researchers.Although,the utilization of robots in catalyst discovery is still in its infancy,in this review we summarize current sway of artificial intelligence in catalyst discovery,briefly describe the application of databases,machine learning models and robots in this field,with emphasis on the consolidation of these monomeric units into a tripartite flow process.We point out current trends of machine learning and hybrid models of first principle calculations(DFT)for generating dataset,which is integrable into autonomous flow process of catalyst discovery.Also,we discuss catalyst discovery for renewable energy related reactions using this tripartite flow process with predetermined descriptors.
基金supported by the National Natural Science Foundation of China(22101182)the Shenzhen Science and Technology Program(Nos.JCYJ20210324095202006,JCYJ20220531095813031,and JCYJ20230807140700001)Guangdong Basic and Applied Basic Research Foundation(2022A1515010318).
文摘Surface functionalization of Cu-based catalysts has demonstrated promising potential for enhancing the electrochemical CO_(2)reduction reaction(CO_(2)RR)toward multi-carbon(C2+)products,primarily by suppressing the parasitic hydrogen evolution reaction and facilitating a localized CO_(2)/CO concentration at the electrode.Building upon this approach,we developed surface-functionalized catalysts with exceptional activity and selectivity for electrocatalytic CO_(2)RR to C_(2+)in a neutral electrolyte.Employing CuO nanoparticles coated with hexaethynylbenzene organic molecules(HEB-CuO NPs),a remarkable C_(2+)Faradaic efficiency of nearly 90%was achieved at an unprecedented current density of 300 mA cm^(-2),and a high FE(>80%)was maintained at a wide range of current densities(100-600 mA cm^(-2))in neutral environments using a flow cell.Furthermore,in a membrane electrode assembly(MEA)electrolyzer,86.14%FEC2+was achieved at a partial current density of 387.6 mA cm^(-2)while maintaining continuous operation for over 50 h at a current density of 200 mA cm^(-2).In-situ spectroscopy studies and molecular dynamics simulations reveal that reducing the coverage of coordinated K⋅H2O water increased the probability of intermediate reactants(CO)interacting with the surface,thereby promoting efficient C-C coupling and enhancing the yield of C_(2+)products.This advancement offers significant potential for optimizing local micro-environments for sustainable and highly efficient C_(2+)production.
基金ANU Futures Scheme,Grant/Award Number:Q4601024National Natural Science Foundation of China,Grant/Award Number:22078054+1 种基金Australian Research Council,Grant/Award Number:DP190100295China Scholarship Council(CSC)Program。
文摘In the past decades,machine learning(ML)has impacted the field of electrocatalysis.Modern researchers have begun to take advantage of ML‐based data‐driven techniques to overcome the computational and experimental limitations to accelerate rational catalyst design.Hence,significant efforts have been made to perform ML to accelerate calculation and aid electrocatalyst design for CO_(2) reduction.This review discusses recent applications of ML to discover,design,and optimize novel electrocatalysts.First,insights into ML aided in accelerating calculation are presented.Then,ML aided in the rational design of the electrocatalyst is introduced,including establishing a data set/data source selection and validation of descriptor selection of ML algorithms validation and predictions of the model.Finally,the opportunities and future challenges are summarized for the future design of electrocatalyst for CO_(2) reduction with the assistance of ML.
基金financially supported by the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(2022C01171)the Postdoctoral Science Foundation of Zhejiang Province(ZJ2022132)+7 种基金the Science and Technology Project of Wenzhou(2022G0253)the National Natural Science Foundation of China(52102188,51772271,and 52072337)the Key Research and Development Program of Zhejiang Province(2021C01030)the Natural Science Foundation of Zhejiang Province(LQ21F040005)the Leading Talent Entrepreneurship Project of Ouhai District,Wenzhou Citythe Young Elite Scientists Sponsorship Program by CAST(YESS20210444)the Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering(2022SZ-TD004)support of Zhejiang University Education Foundation Qizhen Scholar Foundation。
基金The authors would like to acknowledge the financial support provided by the National Natural Science Foundation of China(No.61904141)the Natural Science Foundation of Shaanxi Province(No.2020JQ-295)+5 种基金the China Postdoctoral Science Foundation(2020M673340)the Fundamental Research Funds for the Central Universities(JB210407)the Key Research and Development Program of Shaanxi(Program No.2020GY-252 and No.2021GY277)the Shenzhen-Hong Kong-Macao Technology Research Program(Type C,SGDX2020110309300301)the Fundamental Research Funds for the Central Universitiesthe Innovation Fund of Xidian University.
文摘Monitoring biophysical signals such as body or organ movements and other physical phenomena is necessary for patient rehabilitation.However,stretchable flexible pressure sensors with high sensitivity and a broad range that can meet these requirements are still lacking.Herein,we successfully monitored various vital biophysical features and implemented in-sensor dynamic deep learning for knee rehabilitation using an ultrabroad linear range and highsensitivity stretchable iontronic pressure sensor(SIPS).We optimized the topological structure and material composition of the electrode to build a fully stretching on-skin sensor.The high sensitivity(12.43 kPa^(−1)),ultrabroad linear sensing range(1 MPa),high pressure resolution(6.4 Pa),long-term durability(no decay after 12000 cycles),and excellent stretchability(up to 20%)allow the sensor to maintain operating stability,even in emergency cases with a high sudden impact force(near 1 MPa)applied to the sensor.As a practical demonstration,the SIPS can positively track biophysical signals such as pulse waves,muscle movements,and plantar pressure.Importantly,with the help of a neuro-inspired fully convolutional network algorithm,the SIPS can accurately predict knee joint postures for better rehabilitation after orthopedic surgery.Our SIPS has potential as a promising candidate for wearable electronics and artificial intelligent medical engineering owing to its unique high signal-to-noise ratio and ultrabroad linear range.
基金gratefully express gratitude to all parties who have contributed toward the success of this project,both financially and technically,especially the S&T Innovation 2025 Major Special Programme(Grant No.2018B10022)the Ningbo Commonweal Programme(Grant No.2022S122)funded by the Ningbo Science and Technology Bureau,China,as well as the UNNC FoSE Faculty Inspiration Grant,China+1 种基金the support from the Ningbo Municipal Key Laboratory on Clean Energy Conversion Technologies(2014A22010)as well as the Zhejiang Provincial Key Laboratory for Carbonaceous Wastes Processing and Process Intensification Research funded by the Zhejiang Provincial Department of Science and Technology(2020E10018)support from the ANU Futures Scheme(Q4601024).
文摘Achieving carbon neutrality is an essential part of responding to climate change caused by the deforestation and over-exploitation of natural resources that have accompanied the development of human society.The carbon dioxide reduction reaction(CO_(2)RR)is a promising strategy to capture and convert carbon dioxide(CO_(2))into value-added chemical products.However,the traditional trial-and-error method makes it expensive and time-consuming to understand the deeper mechanism behind the reaction,discover novel catalysts with superior performance and lower cost,and determine optimal support structures and electrolytes for the CO_(2)RR.Emerging machine learning(ML)techniques provide an opportunity to integrate material science and artificial intelligence,which would enable chemists to extract the implicit knowledge behind data,be guided by the insights thereby gained,and be freed from performing repetitive experiments.In this perspective article,we focus on recent ad-vancements in ML-participated CO_(2)RR applications.After a brief introduction to ML techniques and the CO_(2)RR,we first focus on ML-accelerated property prediction for potential CO_(2)RR catalysts.Then we explore ML-aided prediction of catalytic activity and selectivity.This is followed by a discussion about ML-guided catalyst and electrode design.Next,the potential application of ML-assisted experimental synthesis for the CO_(2)RR is discussed.
基金This work was financially supported in part by the National Key R&D Program of China(Nos.2017YFA0303500 and 2020YFA0405800)National Natural Science Foundation of China(NSFC)(Nos.U1932201,U2032113,and 22075264)+3 种基金CAS Collaborative Innovation Program of Hefei Science Center(Nos.2019HSC-CIP002 and 2020HSC-CIP002)the USTC Start-Up Fund,and CAS Interdisciplinary Innovation TeamL.S.acknowledges the support from Key Laboratory of Advanced Energy Materials Chemistry(Ministry of Education)Nankai University(111 projects,B_(12)015).
文摘Despite acknowledgment of structural reconstruction of materials following oxygen evolution reaction (OER) reaction, the role of support during the reconstruction process has been ignored. Given this, we directly in situ transform the residual iron present in raw single-walled carbon nanotubes (SWCNT) into Fe_(2)O_(3) and thus build Fe_(2)O_(3)-CNT as the model system. Intriguingly, an anomalous self-optimization occurred on SWCNT and the derived components show satisfactory electrochemical performance. Soft X-ray absorption spectroscopy (sXAS) analysis and theory calculation correspondingly indicate that self-optimization yields stronger interaction between SWCNT and Fe_(2)O_(3) nanoparticles, where the electrons migrate from Fe_(2)O_(3) to optimized SWCNT. Such polarization will generate a positive charge center and thus boost the OER activity. This finding directly observes the self-optimization of support effect, providing a new perspective for OER and related electrochemical reactions.
基金National Natural Science Foundation of China(No.22008251)Guangdong Basic and Applied Basic Research Foundation(No.2022A1515010318)Shenzhen Science and Technology Program(No.JCYJ20220531095813031).
文摘Improving the catalytic activity of non-noble metal single atom catalysts(SACs)has attracted considerable attention in materials science.Although optimizing the local electronic structure of single atom can greatly improve their catalytic activity,it often involves in-plane modulation and requires high temperatures.Herein,we report a novel strategy to manipulate the local electronic structure of SACs via the modulation of axial Co-S bond anchored onto graphitic carbon nitride(C_(3)N_(4))at room temperature(RT).Each Co atom is bonded to four N atoms and one S atom(Co-(N,S)/C_(3)N_(4)).Owing to the greater electronegativity of S in the Co-S bond,the local electronic structure of the Co atoms is available to be controlled at a relatively moderate level.Consequently,when employed for the photocatalytic hydrogen evolution reaction,the adsorption energy of intermediate hydrogen(H*)on the Co atoms is remarkably low.In the presence of the Co-(N,S)/C_(3)N_(4)SACs,the hydrogen evolution rates reach up to 10 mmol/(g·h),which is nearly 10 and 2.5 times greater than the rates in the presence of previously reported transition metal/C_(3)N_(4)and noble platinum nanoparticles(PtNPs)/C_(3)N_(4)catalysts,respectively.Attributed to the tailorable axial Co-S bond in the SAC,the local electronic structure of the Co atoms can be further optimized for other photocatalytic reactions.This axial coordination engineering strategy is universal in catalyst designing and can be used for a variety of photocatalytic applications.
基金the National Key R&D Program of China(Nos.2020YFA0405800 and 2017YFA0303500)the National Natural Science Foundation of China(NSFC)(Nos.U1932201,U2032113,and 22075264)+3 种基金CAS Collaborative Innovation Program of Hefei Science Center(Nos.2019HSC-CIP002 and 2020HSC-CIP002)USTC Research Funds of the Double First-Class Initiative(No.YD2310002003)Institute of Energy,Hefei Comprehensive Nation Science Center,University Synergy Innovation Program of Anhui Province(GXXT-2020-002)CAS Iterdisciplinary Innovation Team.L.S.acknowledges the support from Key Laboratory of Advanced Energy Materials Chemistry(Ministry of Education),Nankai University(111 project,B12015)。
文摘The phase transformation of catalysts has been extensively observed in heterogeneous catalytic reactions that hinder the long cycling catalysis,and it remains a big challenge to precisely control the active phase during the complex conditions in electrochemical catalysis.Here,we theoretically predict that carbon-based support could achieve the phase engineering regulation of catalysts by suppressing specific phase transformation.Taken single-walled carbon nanotube(SWCNT)as typical support,combined with calculated E-pH(Pourbaix)diagram and advanced synchrotron-based characterizations technologies prove there are two different active phases source from cobalt selenide which demonstrate that the feasibility of using support effect regulating the potential advantageous catalysts.Moreover,it is worth noting that the phase engineering derived Co_(3)O_(4)-SWCNT exhibits a low overpotential of 201 mV for delivering the current density of 10 mA/cm^(2)in electrocatalytic oxygen evolution reaction(OER).Also,it reaches a record current density of 529 mA/cm^(2)at 1.63 V(vs.RHE)in the electrocatalytic urea oxidation reaction(UOR),overwhelming most previously reported catalysts.
基金supported by the National Natural Science Foundation of China (Grant No. 52173234)the Shenzhen Science and Technology Program (Grant Nos. JCYJ20210324102008023 and JSGG202108021534-08024)+3 种基金the Shenzhen-Hong Kong-Macao Technology Research Program(Type C, SGDX2020110309300301)the Natural Science Foundation of Guangdong Province (Grant No. 2022A1515010554)CCF-Tencent Open FundNingbo Municipal Key Laboratory on Clean Energy Conversion Technologies and the Zhejiang Provincial Key Laboratory for Carbonaceous Wastes Processing and Process Intensification Research funded by the Zhejiang Provincial Department of Science and Technology (Grant No. 2020E10018)
文摘Carbon neutrality has been proposed as a solution for the current severe energy and climate crisis caused by the overuse of fossil fuels, and machine learning(ML) has exhibited excellent performance in accelerating related research owing to its powerful capacity for big data processing. This review presents a detailed overview of ML accelerated carbon neutrality research with a focus on energy management, screening of novel energy materials, and ML interatomic potentials(MLIPs), with illustrations of two selected MLIP algorithms: moment tensor potential(MTP) and neural equivariant interatomic potential(NequIP). We conclude by outlining the important role of ML in accelerating the achievement of carbon neutrality from global-scale energy management, unprecedented screening of advanced energy materials in massive chemical space, to the revolution of atomicscale simulations of MLIPs, which has the bright prospect of applications.
基金Basic and Applied Basic Research Foundation of Guangdong Province,Grant/Award Number:2022A1515110206The Chinese University of Hong Kong,Shenzhen,Grant/Award Numbers:K10120220253,YXLH2218。
文摘Protein aggregate species play a pivotal role in the pathology of various degenerative diseases.Their dynamic changes are closely correlated with disease progression,making them promising candidates as diagnostic biomarkers.Given the prevalence of degenerative diseases,growing attention is drawn to develop pragmatic and accessible protein aggregate species detection technology.However,the performance of current detection methods is far from satisfying the requirements of extensive clinical use.In this review,we focus on the design strategies,merits,and potential shortcomings of each class of detection methods.The review is organized into three major parts:native protein sensing,seed amplification,and intricate program,which embody three different but interconnected methodologies.To the best of our knowledge,no systematic review has encompassed the entire workflow,from the molecular level to the apparatus organization.This review emphasizes the feasibility of the methods instead of theoretical detection limitations.We conclude that high selectivity does play a pivotal role,while signal compilation,multilateral profiling,and other patient-oriented strategies(i.e.less invasiveness and assay speed)are also important.
